Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives.
|Original language||English (US)|
|Number of pages||10|
|Journal||American journal of epidemiology|
|State||Published - Jul 1 2022|
Bibliographical noteFunding Information:
This work was supported in part by the National Cancer Institute (grant F31CA239566 awarded to L.J.C.) and the National Library of Medicine (grant R01LM013049 awarded to T.L.L.). T.P.A. was supported by an award from the National Institute of General Medical Sciences (grant P20 GM103644). STRONG cohort data were collected with support from the Patient-Centered Outcomes Research Institute (contract AD-12-11-4532) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant R21HD076387 awarded to M.G.). L.J.C. was also supported, in part, by the National Center for Advancing Translational Sciences (grant TL1TR002540). J.L.G. was supported, in part, by the National Institute of Mental Health (grant R01MH109507).
© 2022 The Author(s).
- epidemiologic methods
- quantitative bias analysis
- study design
- validation substudies
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't